DURHAM, N.C. -- Researchers at Duke University Medical
Center have taught rhesus monkeys to consciously control the
movement of a robot arm in real time, using only signals from
their brains and visual feedback on a video screen. The
scientists said that the animals appeared to operate the robot
arm as if it were their own limb.

The scientists and engineers said their achievement
represents an important step toward technology that could
enable paralyzed people to control "neuroprosthetic" limbs, and
even free-roaming "neurorobots" using brain signals.

Importantly, said the neurobiologists, the technology they
developed for analyzing brain signals from behaving animals
could also greatly improve rehabilitation of people with brain
and spinal cord damage from stroke, disease or trauma. By
understanding the biological factors that control the brain's
adaptability, they said, clinicians could develop improved
drugs and rehabilitation methods for people with such
damage.

The advance was reported in an article published online Oct.
13, 2003, in the Public Library
of Science (PLoS), by neurobiologists led by Miguel
Nicolelis, M.D., who is professor of neurobiology and
co-director of the Duke Center for Neuroengineering. Lead
author of the paper was Jose Carmena, Ph.D., in the Nicolelis
laboratory. Besides Nicolelis, the other senior co-author is
Craig Henriquez, Ph.D., associate professor of biomedical
engineering in the Pratt
School of Engineering, who is also the other center
co-director. The research was funded by the Defense Advanced
Research Projects Agency and the James S. McDonnell
Foundation.

Nicolelis cited numerous researchers at other institutions
whose work has been central to the field of brain-machine
interfaces and in understanding the brain -- and whose insights
helped lead to the latest achievement. They include John
Chapin, Ph.D., State University of New York Health Science
Center, Brooklyn; Eberhard Fetz, Ph.D., University of
Washington, Seattle; Jon Kaas, Ph.D., Vanderbilt University;
Idan Segev, Ph.D., Hebrew University, Jerusalem, and Karen
Moxon, Ph.D., Drexel University.

In previous research, Nicolelis and his colleagues
demonstrated a brain-signal recording and analysis system that
enabled them to decipher brain signals from owl monkeys in
order to control the movement of a robot arm.

The latest work by the Duke researchers is the first to
demonstrate that monkeys can learn to use only visual feedback
and brain signals, without resort to any muscle movement, to
control a mechanical robot arm -- including both reaching and
grasping movements.

In their experiments, the researchers first implanted an
array of microelectrodes -- each smaller than the diameter of a
human hair -- into the frontal and parietal lobes of the brains
of two female rhesus macaque monkeys. They implanted 96
electrodes in one animal and 320 in the other. The researchers
reported their technology of implanting arrays of hundreds of
electrodes and recording from them over long periods in a Sept.
16, 2003, article in the Proceedings of the National Academy of
Sciences.

The researchers chose frontal and parietal areas of the
brain because they are known to be involved in producing
multiple output commands to control complex muscle
movement.

The faint signals from the electrode arrays were detected
and analyzed by the computer system the researchers had
developed to recognize patterns of signals that represented
particular movements by an animal's arm.

In the initial behavioral experiments, the researchers
recorded and analyzed the output signals from the monkeys'
brains as the animals were taught to use a joystick to both
position a cursor over a target on a video screen and to grasp
the joystick with a specified force.

After the animals' initial training, however, the
researchers made the cursor more than a simple display -- now
incorporating into its movement the dynamics, such as inertia
and momentum, of a robot arm functioning in another room. While
the animals' performance initially declined when the robot arm
was included in the feedback loop, they quickly learned to
allow for these dynamics and became proficient in manipulating
the robot-reflecting cursor, found the scientists.

The scientists next removed the joystick, after which the
monkeys continued to move their arms in mid-air to manipulate
and "grab" the cursor, thus controlling the robot arm.

"The most amazing result, though, was that after only a few
days of playing with the robot in this way, the monkey suddenly
realized that she didn't need to move her arm at all," said
Nicolelis. "Her arm muscles went completely quiet, she kept the
arm at her side and she controlled the robot arm using only her
brain and visual feedback. Our analyses of the brain signals
showed that the animal learned to assimilate the robot arm into
her brain as if it was her own arm." Importantly, said
Nicolelis, the experiments included both reaching and grasping
movements, but derived from the same sets of electrodes.

"We knew that the neurons from which we were recording could
encode different kinds of information," said Nicolelis. "But
what was a surprise is that the animal can learn to time the
activity of the neurons to basically control different types of
parameters sequentially. For example, after using a group of
neurons to move the robot to a certain point, these same cells
would then produce the force output that the animals need to
hold an object. None of us had ever encountered an ability like
that."

Also importantly, said Nicolelis, analysis of the signals
from the animals' brains as they learned revealed that the
brain circuitry was actively reorganizing itself to adapt.

"It was extraordinary to see that when we switched the
animal from joystick control to brain control, the
physiological properties of the brain cells changed
immediately. And when we switched the animal back to joystick
control the very next day, the properties changed again.

"Such findings tell us that the brain is so amazingly
adaptable that it can incorporate an external device into its
own 'neuronal space' as a natural extension of the body," said
Nicolelis. "Actually, we see this every day, when we use any
tool, from a pencil to a car. As we learn to use that tool, we
incorporate the properties of that tool into our brain, which
makes us proficient in using it." Said Nicolelis, such findings
of brain plasticity in mature animals and humans are in sharp
contrast to traditional views that only in childhood is the
brain plastic enough to allow for such adaptation.

According to Nicolelis, the finding that their brain-machine
interface system can work in animals will have direct
application to clinical development of neuroprosthetic devices
for paralyzed people.

"There is certainly a great deal of science and engineering
to be done to develop this technology and to create systems
that can be used safely in humans," he said. "However, the
results so far lead us to believe that these brain-machine
interfaces hold enormous promise for restoring function to
paralyzed people."

The researchers are already conducting preliminary studies
of human subjects, in which they are performing analysis of
brain signals to determine whether those signals correlate with
those seen in the animal models. They are also exploring
techniques to increase the longevity of the electrodes beyond
the two years they have currently achieved in animal
studies.

Henriquez and the research team's other biomedical engineers
from Duke's Pratt School of Engineering are also working to
miniaturize the components, to create wireless interfaces and
to develop different grippers, wrists and other mechanical
components of a neuroprosthetic device.

And in their animal studies, the scientists are proceeding
to add an additional source of feedback to the system -- in the
form of a small vibrating device placed on the animal's side
that will tell the animal about another property of the
robot.

Beyond the promise of neuroprosthetic devices, said
Nicolelis, the technology for recording and analyzing signals
from large electrode arrays in the brain will offer an
unprecedented insight into brain function and plasticity.

"We have learned in our studies that this approach will
offer important insights into how the large-scale circuitry of
the brain works," he said. "Since we have total control of the
system, for example, we can change the properties of the robot
arm and watch in real time how the brain adapts."